Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations102599
Missing cells190769
Missing cells (%)7.2%
Duplicate rows541
Duplicate rows (%)0.5%
Total size in memory106.9 MiB
Average record size in memory1.1 KiB

Variable types

Numeric10
Text7
Categorical7
Boolean1
DateTime1

Alerts

country has constant value "United States" Constant
country code has constant value "US" Constant
license has constant value "41662/AL" Constant
Dataset has 541 (0.5%) duplicate rowsDuplicates
long is highly overall correlated with neighbourhood groupHigh correlation
neighbourhood group is highly overall correlated with longHigh correlation
number of reviews is highly overall correlated with reviews per monthHigh correlation
reviews per month is highly overall correlated with number of reviewsHigh correlation
last review has 15893 (15.5%) missing values Missing
reviews per month has 15879 (15.5%) missing values Missing
house_rules has 52131 (50.8%) missing values Missing
license has 102597 (> 99.9%) missing values Missing
minimum nights is highly skewed (γ1 = 86.70130146) Skewed
number of reviews has 15734 (15.3%) zeros Zeros
availability 365 has 23544 (22.9%) zeros Zeros

Reproduction

Analysis started2025-04-11 11:20:33.115468
Analysis finished2025-04-11 11:20:45.473484
Duration12.36 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct102058
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29146235
Minimum1001254
Maximum57367417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size801.7 KiB
2025-04-11T13:20:45.525529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1001254
5-th percentile3834579.8
Q115085814
median29136603
Q343201198
95-th percentile54534173
Maximum57367417
Range56366163
Interquartile range (IQR)28115384

Descriptive statistics

Standard deviation16257506
Coefficient of variation (CV)0.55779094
Kurtosis-1.1978445
Mean29146235
Median Absolute Deviation (MAD)14057692
Skewness0.0031305636
Sum2.9903745 × 1012
Variance2.6430649 × 1014
MonotonicityNot monotonic
2025-04-11T13:20:45.599091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20305326 2
 
< 0.1%
20305878 2
 
< 0.1%
20306430 2
 
< 0.1%
20306983 2
 
< 0.1%
20307535 2
 
< 0.1%
20308087 2
 
< 0.1%
20308639 2
 
< 0.1%
20309192 2
 
< 0.1%
20309744 2
 
< 0.1%
20310296 2
 
< 0.1%
Other values (102048) 102579
> 99.9%
ValueCountFrequency (%)
1001254 1
< 0.1%
1002102 1
< 0.1%
1002403 1
< 0.1%
1002755 1
< 0.1%
1003689 1
< 0.1%
1004098 1
< 0.1%
1004650 1
< 0.1%
1005202 1
< 0.1%
1005754 1
< 0.1%
1006307 1
< 0.1%
ValueCountFrequency (%)
57367417 1
< 0.1%
57366865 1
< 0.1%
57366313 1
< 0.1%
57365760 1
< 0.1%
57365208 1
< 0.1%
57364656 1
< 0.1%
57364103 1
< 0.1%
57363551 1
< 0.1%
57362999 1
< 0.1%
57362446 1
< 0.1%

NAME
Text

Distinct61281
Distinct (%)59.9%
Missing250
Missing (%)0.2%
Memory size8.7 MiB
2025-04-11T13:20:45.770737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length248
Median length147
Mean length37.572942
Min length1

Characters and Unicode

Total characters3845553
Distinct characters988
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32463 ?
Unique (%)31.7%

Sample

1st rowClean & quiet apt home by the park
2nd rowSkylit Midtown Castle
3rd rowTHE VILLAGE OF HARLEM....NEW YORK !
4th rowEntire Apt: Spacious Studio/Loft by central park
5th rowLarge Cozy 1 BR Apartment In Midtown East
ValueCountFrequency (%)
in 34825
 
5.5%
room 21281
 
3.3%
17932
 
2.8%
bedroom 16132
 
2.5%
private 15532
 
2.4%
apartment 14093
 
2.2%
cozy 10567
 
1.7%
apt 9361
 
1.5%
studio 8667
 
1.4%
brooklyn 8589
 
1.4%
Other values (16029) 478984
75.3%
2025-04-11T13:20:46.094513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
536944
 
14.0%
e 268418
 
7.0%
o 262373
 
6.8%
t 225330
 
5.9%
a 220457
 
5.7%
r 208625
 
5.4%
n 201132
 
5.2%
i 200940
 
5.2%
l 109455
 
2.8%
m 104563
 
2.7%
Other values (978) 1507316
39.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3845553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
536944
 
14.0%
e 268418
 
7.0%
o 262373
 
6.8%
t 225330
 
5.9%
a 220457
 
5.7%
r 208625
 
5.4%
n 201132
 
5.2%
i 200940
 
5.2%
l 109455
 
2.8%
m 104563
 
2.7%
Other values (978) 1507316
39.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3845553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
536944
 
14.0%
e 268418
 
7.0%
o 262373
 
6.8%
t 225330
 
5.9%
a 220457
 
5.7%
r 208625
 
5.4%
n 201132
 
5.2%
i 200940
 
5.2%
l 109455
 
2.8%
m 104563
 
2.7%
Other values (978) 1507316
39.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3845553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
536944
 
14.0%
e 268418
 
7.0%
o 262373
 
6.8%
t 225330
 
5.9%
a 220457
 
5.7%
r 208625
 
5.4%
n 201132
 
5.2%
i 200940
 
5.2%
l 109455
 
2.8%
m 104563
 
2.7%
Other values (978) 1507316
39.2%

host id
Real number (ℝ)

Distinct102057
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9254111 × 1010
Minimum1.2360052 × 108
Maximum9.8763129 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size801.7 KiB
2025-04-11T13:20:46.341222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.2360052 × 108
5-th percentile4.9028193 × 109
Q12.4583328 × 1010
median4.9117739 × 1010
Q37.3996496 × 1010
95-th percentile9.3813867 × 1010
Maximum9.8763129 × 1010
Range9.8639529 × 1010
Interquartile range (IQR)4.9413167 × 1010

Descriptive statistics

Standard deviation2.8538997 × 1010
Coefficient of variation (CV)0.57942364
Kurtosis-1.2021324
Mean4.9254111 × 1010
Median Absolute Deviation (MAD)2.471931 × 1010
Skewness0.0063946041
Sum5.0534226 × 1015
Variance8.1447433 × 1020
MonotonicityNot monotonic
2025-04-11T13:20:46.411783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.873059513 × 10102
 
< 0.1%
5.035876034 × 10102
 
< 0.1%
8.946153186 × 10102
 
< 0.1%
8.65496707 × 10102
 
< 0.1%
1.567538893 × 10102
 
< 0.1%
4.167243775 × 10102
 
< 0.1%
3.250262233 × 10102
 
< 0.1%
2825302502 2
 
< 0.1%
6.098446376 × 10102
 
< 0.1%
3.931100187 × 10102
 
< 0.1%
Other values (102047) 102579
> 99.9%
ValueCountFrequency (%)
123600518 1
< 0.1%
124039648 1
< 0.1%
124472619 1
< 0.1%
129756565 1
< 0.1%
130349612 1
< 0.1%
130593431 1
< 0.1%
131602089 1
< 0.1%
132238305 1
< 0.1%
133264740 1
< 0.1%
134452120 1
< 0.1%
ValueCountFrequency (%)
9.876312902 × 10101
< 0.1%
9.876268323 × 10101
< 0.1%
9.876266181 × 10101
< 0.1%
9.876096863 × 10101
< 0.1%
9.875813627 × 10101
< 0.1%
9.875797561 × 10101
< 0.1%
9.875795011 × 10101
< 0.1%
9.875764603 × 10101
< 0.1%
9.87574594 × 10101
< 0.1%
9.875733136 × 10101
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing289
Missing (%)0.3%
Memory size5.7 MiB
unconfirmed
51200 
verified
51110 

Length

Max length11
Median length11
Mean length9.5013195
Min length8

Characters and Unicode

Total characters972080
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunconfirmed
2nd rowverified
3rd rowunconfirmed
4th rowverified
5th rowverified

Common Values

ValueCountFrequency (%)
unconfirmed 51200
49.9%
verified 51110
49.8%
(Missing) 289
 
0.3%

Length

2025-04-11T13:20:46.475337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T13:20:46.515871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
unconfirmed 51200
50.0%
verified 51110
50.0%

Most occurring characters

ValueCountFrequency (%)
i 153420
15.8%
e 153420
15.8%
n 102400
10.5%
d 102310
10.5%
f 102310
10.5%
r 102310
10.5%
u 51200
 
5.3%
c 51200
 
5.3%
o 51200
 
5.3%
m 51200
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 972080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 153420
15.8%
e 153420
15.8%
n 102400
10.5%
d 102310
10.5%
f 102310
10.5%
r 102310
10.5%
u 51200
 
5.3%
c 51200
 
5.3%
o 51200
 
5.3%
m 51200
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 972080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 153420
15.8%
e 153420
15.8%
n 102400
10.5%
d 102310
10.5%
f 102310
10.5%
r 102310
10.5%
u 51200
 
5.3%
c 51200
 
5.3%
o 51200
 
5.3%
m 51200
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 972080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 153420
15.8%
e 153420
15.8%
n 102400
10.5%
d 102310
10.5%
f 102310
10.5%
r 102310
10.5%
u 51200
 
5.3%
c 51200
 
5.3%
o 51200
 
5.3%
m 51200
 
5.3%
Distinct13190
Distinct (%)12.9%
Missing406
Missing (%)0.4%
Memory size5.4 MiB
2025-04-11T13:20:46.648484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length32
Mean length6.1730158
Min length1

Characters and Unicode

Total characters630839
Distinct characters215
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3932 ?
Unique (%)3.8%

Sample

1st rowMadaline
2nd rowJenna
3rd rowElise
4th rowGarry
5th rowLyndon
ValueCountFrequency (%)
2325
 
2.0%
and 1382
 
1.2%
michael 967
 
0.8%
david 853
 
0.7%
sonder 733
 
0.6%
alex 670
 
0.6%
john 664
 
0.6%
nyc 545
 
0.5%
laura 541
 
0.5%
daniel 515
 
0.4%
Other values (11712) 105512
92.0%
2025-04-11T13:20:46.860164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 79228
 
12.6%
e 60413
 
9.6%
i 51236
 
8.1%
n 50665
 
8.0%
r 37615
 
6.0%
l 32350
 
5.1%
o 27211
 
4.3%
t 20228
 
3.2%
s 19512
 
3.1%
h 19130
 
3.0%
Other values (205) 233251
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 630839
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 79228
 
12.6%
e 60413
 
9.6%
i 51236
 
8.1%
n 50665
 
8.0%
r 37615
 
6.0%
l 32350
 
5.1%
o 27211
 
4.3%
t 20228
 
3.2%
s 19512
 
3.1%
h 19130
 
3.0%
Other values (205) 233251
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 630839
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 79228
 
12.6%
e 60413
 
9.6%
i 51236
 
8.1%
n 50665
 
8.0%
r 37615
 
6.0%
l 32350
 
5.1%
o 27211
 
4.3%
t 20228
 
3.2%
s 19512
 
3.1%
h 19130
 
3.0%
Other values (205) 233251
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 630839
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 79228
 
12.6%
e 60413
 
9.6%
i 51236
 
8.1%
n 50665
 
8.0%
r 37615
 
6.0%
l 32350
 
5.1%
o 27211
 
4.3%
t 20228
 
3.2%
s 19512
 
3.1%
h 19130
 
3.0%
Other values (205) 233251
37.0%

neighbourhood group
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing29
Missing (%)< 0.1%
Memory size5.6 MiB
Manhattan
43792 
Brooklyn
41842 
Queens
13267 
Bronx
 
2712
Staten Island
 
955
Other values (2)
 
2

Length

Max length13
Median length9
Mean length8.1354782
Min length5

Characters and Unicode

Total characters834456
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowBrooklyn
2nd rowManhattan
3rd rowManhattan
4th rowBrooklyn
5th rowManhattan

Common Values

ValueCountFrequency (%)
Manhattan 43792
42.7%
Brooklyn 41842
40.8%
Queens 13267
 
12.9%
Bronx 2712
 
2.6%
Staten Island 955
 
0.9%
manhatan 1
 
< 0.1%
brookln 1
 
< 0.1%
(Missing) 29
 
< 0.1%

Length

2025-04-11T13:20:46.915711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T13:20:46.963752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 43792
42.3%
brooklyn 41842
40.4%
queens 13267
 
12.8%
bronx 2712
 
2.6%
staten 955
 
0.9%
island 955
 
0.9%
manhatan 1
 
< 0.1%
brookln 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 147318
17.7%
a 133289
16.0%
t 89495
10.7%
o 86398
10.4%
r 44555
 
5.3%
B 44554
 
5.3%
h 43793
 
5.2%
M 43792
 
5.2%
l 42798
 
5.1%
k 41843
 
5.0%
Other values (12) 116621
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 834456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 147318
17.7%
a 133289
16.0%
t 89495
10.7%
o 86398
10.4%
r 44555
 
5.3%
B 44554
 
5.3%
h 43793
 
5.2%
M 43792
 
5.2%
l 42798
 
5.1%
k 41843
 
5.0%
Other values (12) 116621
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 834456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 147318
17.7%
a 133289
16.0%
t 89495
10.7%
o 86398
10.4%
r 44555
 
5.3%
B 44554
 
5.3%
h 43793
 
5.2%
M 43792
 
5.2%
l 42798
 
5.1%
k 41843
 
5.0%
Other values (12) 116621
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 834456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 147318
17.7%
a 133289
16.0%
t 89495
10.7%
o 86398
10.4%
r 44555
 
5.3%
B 44554
 
5.3%
h 43793
 
5.2%
M 43792
 
5.2%
l 42798
 
5.1%
k 41843
 
5.0%
Other values (12) 116621
14.0%
Distinct224
Distinct (%)0.2%
Missing16
Missing (%)< 0.1%
Memory size6.0 MiB
2025-04-11T13:20:47.103872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length18
Mean length11.871265
Min length4

Characters and Unicode

Total characters1217790
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowKensington
2nd rowMidtown
3rd rowHarlem
4th rowClinton Hill
5th rowEast Harlem
ValueCountFrequency (%)
east 13786
 
8.3%
side 9488
 
5.7%
bedford-stuyvesant 7937
 
4.8%
harlem 7807
 
4.7%
williamsburg 7775
 
4.7%
upper 7540
 
4.6%
heights 7346
 
4.4%
village 6069
 
3.7%
west 5409
 
3.3%
bushwick 4982
 
3.0%
Other values (236) 87336
52.8%
2025-04-11T13:20:47.291031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 111429
 
9.2%
i 86709
 
7.1%
s 83195
 
6.8%
t 80964
 
6.6%
a 79684
 
6.5%
l 70746
 
5.8%
r 70486
 
5.8%
62892
 
5.2%
n 55186
 
4.5%
o 51331
 
4.2%
Other values (44) 465168
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1217790
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 111429
 
9.2%
i 86709
 
7.1%
s 83195
 
6.8%
t 80964
 
6.6%
a 79684
 
6.5%
l 70746
 
5.8%
r 70486
 
5.8%
62892
 
5.2%
n 55186
 
4.5%
o 51331
 
4.2%
Other values (44) 465168
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1217790
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 111429
 
9.2%
i 86709
 
7.1%
s 83195
 
6.8%
t 80964
 
6.6%
a 79684
 
6.5%
l 70746
 
5.8%
r 70486
 
5.8%
62892
 
5.2%
n 55186
 
4.5%
o 51331
 
4.2%
Other values (44) 465168
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1217790
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 111429
 
9.2%
i 86709
 
7.1%
s 83195
 
6.8%
t 80964
 
6.6%
a 79684
 
6.5%
l 70746
 
5.8%
r 70486
 
5.8%
62892
 
5.2%
n 55186
 
4.5%
o 51331
 
4.2%
Other values (44) 465168
38.2%

lat
Real number (ℝ)

Distinct21991
Distinct (%)21.4%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40.728094
Minimum40.49979
Maximum40.91697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size801.7 KiB
2025-04-11T13:20:47.349581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40.49979
5-th percentile40.64328
Q140.68874
median40.72229
Q340.76276
95-th percentile40.82665
Maximum40.91697
Range0.41718
Interquartile range (IQR)0.07402

Descriptive statistics

Standard deviation0.055856516
Coefficient of variation (CV)0.0013714493
Kurtosis0.14772584
Mean40.728094
Median Absolute Deviation (MAD)0.03672
Skewness0.23082248
Sum4178335.9
Variance0.0031199504
MonotonicityNot monotonic
2025-04-11T13:20:47.417638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.76411 36
 
< 0.1%
40.71813 32
 
< 0.1%
40.76125 28
 
< 0.1%
40.73756 27
 
< 0.1%
40.7244 25
 
< 0.1%
40.71353 25
 
< 0.1%
40.76106 25
 
< 0.1%
40.6898 24
 
< 0.1%
40.76189 23
 
< 0.1%
40.69175 23
 
< 0.1%
Other values (21981) 102323
99.7%
ValueCountFrequency (%)
40.49979 1
< 0.1%
40.50456 1
< 0.1%
40.50641 1
< 0.1%
40.50708 2
< 0.1%
40.50863 1
< 0.1%
40.50868 2
< 0.1%
40.50873 2
< 0.1%
40.50943 1
< 0.1%
40.51133 2
< 0.1%
40.52211 2
< 0.1%
ValueCountFrequency (%)
40.91697 1
< 0.1%
40.91685 1
< 0.1%
40.9131 1
< 0.1%
40.91306 1
< 0.1%
40.91248 1
< 0.1%
40.91234 1
< 0.1%
40.91169 2
< 0.1%
40.91167 1
< 0.1%
40.9116 1
< 0.1%
40.91139 1
< 0.1%

long
Real number (ℝ)

High correlation 

Distinct17774
Distinct (%)17.3%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-73.949644
Minimum-74.24984
Maximum-73.70522
Zeros0
Zeros (%)0.0%
Negative102591
Negative (%)> 99.9%
Memory size801.7 KiB
2025-04-11T13:20:47.486197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.24984
5-th percentile-74.00384
Q1-73.98258
median-73.95444
Q3-73.93235
95-th percentile-73.851195
Maximum-73.70522
Range0.54462
Interquartile range (IQR)0.05023

Descriptive statistics

Standard deviation0.049521264
Coefficient of variation (CV)-0.00066966197
Kurtosis4.3389564
Mean-73.949644
Median Absolute Deviation (MAD)0.02595
Skewness1.2437242
Sum-7586567.9
Variance0.0024523556
MonotonicityNot monotonic
2025-04-11T13:20:47.559759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.99371 44
 
< 0.1%
-73.9535 40
 
< 0.1%
-73.95427 37
 
< 0.1%
-73.94791 37
 
< 0.1%
-73.95677 34
 
< 0.1%
-73.94977 34
 
< 0.1%
-73.95675 32
 
< 0.1%
-73.94513 32
 
< 0.1%
-73.95732 31
 
< 0.1%
-73.943 31
 
< 0.1%
Other values (17764) 102239
99.6%
ValueCountFrequency (%)
-74.24984 1
< 0.1%
-74.24442 1
< 0.1%
-74.24285 2
< 0.1%
-74.24135 1
< 0.1%
-74.24084 1
< 0.1%
-74.23986 2
< 0.1%
-74.23914 2
< 0.1%
-74.23803 2
< 0.1%
-74.23059 1
< 0.1%
-74.21238 1
< 0.1%
ValueCountFrequency (%)
-73.70522 1
 
< 0.1%
-73.70524 1
 
< 0.1%
-73.71087 1
 
< 0.1%
-73.71299 3
< 0.1%
-73.7169 1
 
< 0.1%
-73.71795 1
 
< 0.1%
-73.71829 1
 
< 0.1%
-73.71928 1
 
< 0.1%
-73.71997 1
 
< 0.1%
-73.72122 1
 
< 0.1%

country
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing532
Missing (%)0.5%
Memory size6.1 MiB
United States
102067 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters1326871
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States 102067
99.5%
(Missing) 532
 
0.5%

Length

2025-04-11T13:20:47.622313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T13:20:47.653339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
united 102067
50.0%
states 102067
50.0%

Most occurring characters

ValueCountFrequency (%)
t 306201
23.1%
e 204134
15.4%
n 102067
 
7.7%
U 102067
 
7.7%
i 102067
 
7.7%
d 102067
 
7.7%
102067
 
7.7%
S 102067
 
7.7%
a 102067
 
7.7%
s 102067
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1326871
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 306201
23.1%
e 204134
15.4%
n 102067
 
7.7%
U 102067
 
7.7%
i 102067
 
7.7%
d 102067
 
7.7%
102067
 
7.7%
S 102067
 
7.7%
a 102067
 
7.7%
s 102067
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1326871
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 306201
23.1%
e 204134
15.4%
n 102067
 
7.7%
U 102067
 
7.7%
i 102067
 
7.7%
d 102067
 
7.7%
102067
 
7.7%
S 102067
 
7.7%
a 102067
 
7.7%
s 102067
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1326871
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 306201
23.1%
e 204134
15.4%
n 102067
 
7.7%
U 102067
 
7.7%
i 102067
 
7.7%
d 102067
 
7.7%
102067
 
7.7%
S 102067
 
7.7%
a 102067
 
7.7%
s 102067
 
7.7%

country code
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing131
Missing (%)0.1%
Memory size5.0 MiB
US
102468 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters204936
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US 102468
99.9%
(Missing) 131
 
0.1%

Length

2025-04-11T13:20:47.691371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T13:20:47.721898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
us 102468
100.0%

Most occurring characters

ValueCountFrequency (%)
U 102468
50.0%
S 102468
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204936
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 102468
50.0%
S 102468
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204936
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 102468
50.0%
S 102468
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204936
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 102468
50.0%
S 102468
50.0%
Distinct2
Distinct (%)< 0.1%
Missing105
Missing (%)0.1%
Memory size3.5 MiB
False
51474 
True
51020 
(Missing)
 
105
ValueCountFrequency (%)
False 51474
50.2%
True 51020
49.7%
(Missing) 105
 
0.1%
2025-04-11T13:20:47.741914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing76
Missing (%)0.1%
Memory size5.5 MiB
moderate
34343 
strict
34106 
flexible
34074 

Length

Max length8
Median length8
Mean length7.3346664
Min length6

Characters and Unicode

Total characters751972
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstrict
2nd rowmoderate
3rd rowflexible
4th rowmoderate
5th rowmoderate

Common Values

ValueCountFrequency (%)
moderate 34343
33.5%
strict 34106
33.2%
flexible 34074
33.2%
(Missing) 76
 
0.1%

Length

2025-04-11T13:20:47.791456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T13:20:47.834493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
moderate 34343
33.5%
strict 34106
33.3%
flexible 34074
33.2%

Most occurring characters

ValueCountFrequency (%)
e 136834
18.2%
t 102555
13.6%
r 68449
9.1%
i 68180
9.1%
l 68148
9.1%
o 34343
 
4.6%
a 34343
 
4.6%
d 34343
 
4.6%
m 34343
 
4.6%
s 34106
 
4.5%
Other values (4) 136328
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 751972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 136834
18.2%
t 102555
13.6%
r 68449
9.1%
i 68180
9.1%
l 68148
9.1%
o 34343
 
4.6%
a 34343
 
4.6%
d 34343
 
4.6%
m 34343
 
4.6%
s 34106
 
4.5%
Other values (4) 136328
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 751972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 136834
18.2%
t 102555
13.6%
r 68449
9.1%
i 68180
9.1%
l 68148
9.1%
o 34343
 
4.6%
a 34343
 
4.6%
d 34343
 
4.6%
m 34343
 
4.6%
s 34106
 
4.5%
Other values (4) 136328
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 751972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 136834
18.2%
t 102555
13.6%
r 68449
9.1%
i 68180
9.1%
l 68148
9.1%
o 34343
 
4.6%
a 34343
 
4.6%
d 34343
 
4.6%
m 34343
 
4.6%
s 34106
 
4.5%
Other values (4) 136328
18.1%

room type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
Entire home/apt
53701 
Private room
46556 
Shared room
 
2226
Hotel room
 
116

Length

Max length15
Median length15
Mean length13.546263
Min length10

Characters and Unicode

Total characters1389833
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowPrivate room
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 53701
52.3%
Private room 46556
45.4%
Shared room 2226
 
2.2%
Hotel room 116
 
0.1%

Length

2025-04-11T13:20:47.884036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T13:20:47.923069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
entire 53701
26.2%
home/apt 53701
26.2%
room 48898
23.8%
private 46556
22.7%
shared 2226
 
1.1%
hotel 116
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 156300
11.2%
t 154074
11.1%
o 151613
10.9%
r 151381
10.9%
102599
 
7.4%
m 102599
 
7.4%
a 102483
 
7.4%
i 100257
 
7.2%
h 55927
 
4.0%
n 53701
 
3.9%
Other values (9) 258899
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1389833
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 156300
11.2%
t 154074
11.1%
o 151613
10.9%
r 151381
10.9%
102599
 
7.4%
m 102599
 
7.4%
a 102483
 
7.4%
i 100257
 
7.2%
h 55927
 
4.0%
n 53701
 
3.9%
Other values (9) 258899
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1389833
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 156300
11.2%
t 154074
11.1%
o 151613
10.9%
r 151381
10.9%
102599
 
7.4%
m 102599
 
7.4%
a 102483
 
7.4%
i 100257
 
7.2%
h 55927
 
4.0%
n 53701
 
3.9%
Other values (9) 258899
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1389833
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 156300
11.2%
t 154074
11.1%
o 151613
10.9%
r 151381
10.9%
102599
 
7.4%
m 102599
 
7.4%
a 102483
 
7.4%
i 100257
 
7.2%
h 55927
 
4.0%
n 53701
 
3.9%
Other values (9) 258899
18.6%

Construction year
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing214
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2012.4875
Minimum2003
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size801.7 KiB
2025-04-11T13:20:47.968107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2003
Q12007
median2012
Q32017
95-th percentile2022
Maximum2022
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7655564
Coefficient of variation (CV)0.0028648906
Kurtosis-1.2071988
Mean2012.4875
Median Absolute Deviation (MAD)5
Skewness0.0057330534
Sum2.0604853 × 108
Variance33.241641
MonotonicityNot monotonic
2025-04-11T13:20:48.022153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2014 5243
 
5.1%
2008 5225
 
5.1%
2006 5223
 
5.1%
2019 5201
 
5.1%
2009 5166
 
5.0%
2020 5158
 
5.0%
2010 5155
 
5.0%
2022 5134
 
5.0%
2005 5132
 
5.0%
2012 5131
 
5.0%
Other values (10) 50617
49.3%
ValueCountFrequency (%)
2003 5125
5.0%
2004 5037
4.9%
2005 5132
5.0%
2006 5223
5.1%
2007 5106
5.0%
2008 5225
5.1%
2009 5166
5.0%
2010 5155
5.0%
2011 5058
4.9%
2012 5131
5.0%
ValueCountFrequency (%)
2022 5134
5.0%
2021 5039
4.9%
2020 5158
5.0%
2019 5201
5.1%
2018 5057
4.9%
2017 5066
4.9%
2016 5017
4.9%
2015 5094
5.0%
2014 5243
5.1%
2013 5018
4.9%

price
Text

Distinct1151
Distinct (%)1.1%
Missing247
Missing (%)0.2%
Memory size5.3 MiB
2025-04-11T13:20:48.211814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.3069505
Min length4

Characters and Unicode

Total characters543177
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$966
2nd row$142
3rd row$620
4th row$368
5th row$204
ValueCountFrequency (%)
206 137
 
0.1%
1,056 132
 
0.1%
481 129
 
0.1%
833 128
 
0.1%
573 127
 
0.1%
972 125
 
0.1%
430 122
 
0.1%
138 122
 
0.1%
406 120
 
0.1%
283 120
 
0.1%
Other values (1141) 101090
98.8%
2025-04-11T13:20:48.444513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
$ 102352
18.8%
102352
18.8%
1 55389
10.2%
7 29921
 
5.5%
5 29898
 
5.5%
8 29636
 
5.5%
9 29587
 
5.4%
6 29560
 
5.4%
0 29545
 
5.4%
3 29034
 
5.3%
Other values (3) 75903
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 543177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
$ 102352
18.8%
102352
18.8%
1 55389
10.2%
7 29921
 
5.5%
5 29898
 
5.5%
8 29636
 
5.5%
9 29587
 
5.4%
6 29560
 
5.4%
0 29545
 
5.4%
3 29034
 
5.3%
Other values (3) 75903
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 543177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
$ 102352
18.8%
102352
18.8%
1 55389
10.2%
7 29921
 
5.5%
5 29898
 
5.5%
8 29636
 
5.5%
9 29587
 
5.4%
6 29560
 
5.4%
0 29545
 
5.4%
3 29034
 
5.3%
Other values (3) 75903
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 543177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
$ 102352
18.8%
102352
18.8%
1 55389
10.2%
7 29921
 
5.5%
5 29898
 
5.5%
8 29636
 
5.5%
9 29587
 
5.4%
6 29560
 
5.4%
0 29545
 
5.4%
3 29034
 
5.3%
Other values (3) 75903
14.0%
Distinct231
Distinct (%)0.2%
Missing273
Missing (%)0.3%
Memory size5.2 MiB
2025-04-11T13:20:48.628170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.6113305
Min length4

Characters and Unicode

Total characters471859
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$193
2nd row$28
3rd row$124
4th row$74
5th row$41
ValueCountFrequency (%)
41 526
 
0.5%
216 524
 
0.5%
81 519
 
0.5%
177 518
 
0.5%
57 513
 
0.5%
207 506
 
0.5%
127 503
 
0.5%
128 503
 
0.5%
92 495
 
0.5%
178 495
 
0.5%
Other values (221) 97224
95.0%
2025-04-11T13:20:48.866372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
$ 102326
21.7%
102326
21.7%
1 68261
14.5%
2 41800
8.9%
3 23046
 
4.9%
4 19634
 
4.2%
9 19271
 
4.1%
8 19207
 
4.1%
0 19134
 
4.1%
6 19009
 
4.0%
Other values (2) 37845
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 471859
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
$ 102326
21.7%
102326
21.7%
1 68261
14.5%
2 41800
8.9%
3 23046
 
4.9%
4 19634
 
4.2%
9 19271
 
4.1%
8 19207
 
4.1%
0 19134
 
4.1%
6 19009
 
4.0%
Other values (2) 37845
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 471859
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
$ 102326
21.7%
102326
21.7%
1 68261
14.5%
2 41800
8.9%
3 23046
 
4.9%
4 19634
 
4.2%
9 19271
 
4.1%
8 19207
 
4.1%
0 19134
 
4.1%
6 19009
 
4.0%
Other values (2) 37845
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 471859
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
$ 102326
21.7%
102326
21.7%
1 68261
14.5%
2 41800
8.9%
3 23046
 
4.9%
4 19634
 
4.2%
9 19271
 
4.1%
8 19207
 
4.1%
0 19134
 
4.1%
6 19009
 
4.0%
Other values (2) 37845
 
8.0%

minimum nights
Real number (ℝ)

Skewed 

Distinct153
Distinct (%)0.1%
Missing409
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean8.135845
Minimum-1223
Maximum5645
Zeros0
Zeros (%)0.0%
Negative13
Negative (%)< 0.1%
Memory size801.7 KiB
2025-04-11T13:20:48.924922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1223
5-th percentile1
Q12
median3
Q35
95-th percentile30
Maximum5645
Range6868
Interquartile range (IQR)3

Descriptive statistics

Standard deviation30.553781
Coefficient of variation (CV)3.7554527
Kurtosis13620.827
Mean8.135845
Median Absolute Deviation (MAD)2
Skewness86.701301
Sum831402
Variance933.53355
MonotonicityNot monotonic
2025-04-11T13:20:48.991979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 25421
24.8%
2 23604
23.0%
3 16113
15.7%
30 11653
11.4%
4 6625
 
6.5%
5 6051
 
5.9%
7 4039
 
3.9%
6 1538
 
1.5%
14 1077
 
1.0%
10 932
 
0.9%
Other values (143) 5137
 
5.0%
ValueCountFrequency (%)
-1223 1
 
< 0.1%
-365 1
 
< 0.1%
-200 1
 
< 0.1%
-125 1
 
< 0.1%
-12 1
 
< 0.1%
-10 4
< 0.1%
-5 1
 
< 0.1%
-3 1
 
< 0.1%
-2 1
 
< 0.1%
-1 1
 
< 0.1%
ValueCountFrequency (%)
5645 1
 
< 0.1%
3455 1
 
< 0.1%
2645 1
 
< 0.1%
1250 1
 
< 0.1%
1000 2
 
< 0.1%
999 4
< 0.1%
954 1
 
< 0.1%
825 1
 
< 0.1%
500 8
< 0.1%
480 2
 
< 0.1%

number of reviews
Real number (ℝ)

High correlation  Zeros 

Distinct476
Distinct (%)0.5%
Missing183
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean27.483743
Minimum0
Maximum1024
Zeros15734
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size801.7 KiB
2025-04-11T13:20:49.063540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q330
95-th percentile125
Maximum1024
Range1024
Interquartile range (IQR)29

Descriptive statistics

Standard deviation49.508954
Coefficient of variation (CV)1.8013905
Kurtosis25.029862
Mean27.483743
Median Absolute Deviation (MAD)7
Skewness3.8393962
Sum2814775
Variance2451.1365
MonotonicityNot monotonic
2025-04-11T13:20:49.134600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15734
 
15.3%
1 10408
 
10.1%
2 7175
 
7.0%
3 5375
 
5.2%
4 4151
 
4.0%
5 3426
 
3.3%
6 2922
 
2.8%
7 2527
 
2.5%
8 2363
 
2.3%
9 2118
 
2.1%
Other values (466) 46217
45.0%
ValueCountFrequency (%)
0 15734
15.3%
1 10408
10.1%
2 7175
7.0%
3 5375
 
5.2%
4 4151
 
4.0%
5 3426
 
3.3%
6 2922
 
2.8%
7 2527
 
2.5%
8 2363
 
2.3%
9 2118
 
2.1%
ValueCountFrequency (%)
1024 1
< 0.1%
1010 1
< 0.1%
966 1
< 0.1%
884 1
< 0.1%
849 1
< 0.1%
797 1
< 0.1%
776 1
< 0.1%
738 1
< 0.1%
698 1
< 0.1%
679 1
< 0.1%

last review
Date

Missing 

Distinct2477
Distinct (%)2.9%
Missing15893
Missing (%)15.5%
Memory size801.7 KiB
Minimum2012-07-11 00:00:00
Maximum2058-06-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-11T13:20:49.199155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:49.271717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews per month
Real number (ℝ)

High correlation  Missing 

Distinct1016
Distinct (%)1.2%
Missing15879
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean1.3740219
Minimum0.01
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size801.7 KiB
2025-04-11T13:20:49.346781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.05
Q10.22
median0.74
Q32
95-th percentile4.55
Maximum90
Range89.99
Interquartile range (IQR)1.78

Descriptive statistics

Standard deviation1.7466214
Coefficient of variation (CV)1.2711743
Kurtosis216.81143
Mean1.3740219
Median Absolute Deviation (MAD)0.62
Skewness7.026187
Sum119155.18
Variance3.0506863
MonotonicityNot monotonic
2025-04-11T13:20:49.415339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03 1666
 
1.6%
0.05 1490
 
1.5%
1 1455
 
1.4%
0.04 1270
 
1.2%
0.09 1269
 
1.2%
0.08 1259
 
1.2%
0.16 1258
 
1.2%
0.06 1193
 
1.2%
0.02 1164
 
1.1%
0.11 1141
 
1.1%
Other values (1006) 73555
71.7%
(Missing) 15879
 
15.5%
ValueCountFrequency (%)
0.01 67
 
0.1%
0.02 1164
1.1%
0.03 1666
1.6%
0.04 1270
1.2%
0.05 1490
1.5%
0.06 1193
1.2%
0.07 1085
1.1%
0.08 1259
1.2%
0.09 1269
1.2%
0.1 987
1.0%
ValueCountFrequency (%)
90 1
< 0.1%
84.49 1
< 0.1%
65.74 1
< 0.1%
58.5 2
< 0.1%
57.31 1
< 0.1%
47.11 1
< 0.1%
44.63 1
< 0.1%
34.46 1
< 0.1%
33.08 1
< 0.1%
30.51 1
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing326
Missing (%)0.3%
Memory size5.1 MiB
5.0
23369 
4.0
23329 
3.0
23265 
2.0
23098 
1.0
9212 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters306819
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row5.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0 23369
22.8%
4.0 23329
22.7%
3.0 23265
22.7%
2.0 23098
22.5%
1.0 9212
 
9.0%
(Missing) 326
 
0.3%

Length

2025-04-11T13:20:49.475390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T13:20:49.516926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5.0 23369
22.8%
4.0 23329
22.8%
3.0 23265
22.7%
2.0 23098
22.6%
1.0 9212
 
9.0%

Most occurring characters

ValueCountFrequency (%)
. 102273
33.3%
0 102273
33.3%
5 23369
 
7.6%
4 23329
 
7.6%
3 23265
 
7.6%
2 23098
 
7.5%
1 9212
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 306819
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 102273
33.3%
0 102273
33.3%
5 23369
 
7.6%
4 23329
 
7.6%
3 23265
 
7.6%
2 23098
 
7.5%
1 9212
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 306819
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 102273
33.3%
0 102273
33.3%
5 23369
 
7.6%
4 23329
 
7.6%
3 23265
 
7.6%
2 23098
 
7.5%
1 9212
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 306819
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 102273
33.3%
0 102273
33.3%
5 23369
 
7.6%
4 23329
 
7.6%
3 23265
 
7.6%
2 23098
 
7.5%
1 9212
 
3.0%
Distinct78
Distinct (%)0.1%
Missing319
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean7.9366054
Minimum1
Maximum332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size801.7 KiB
2025-04-11T13:20:49.579479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile29
Maximum332
Range331
Interquartile range (IQR)1

Descriptive statistics

Standard deviation32.21878
Coefficient of variation (CV)4.0595165
Kurtosis58.678982
Mean7.9366054
Median Absolute Deviation (MAD)0
Skewness7.2204463
Sum811756
Variance1038.0498
MonotonicityNot monotonic
2025-04-11T13:20:49.648037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 63429
61.8%
2 14445
 
14.1%
3 6577
 
6.4%
4 3552
 
3.5%
5 1995
 
1.9%
6 1477
 
1.4%
7 996
 
1.0%
8 958
 
0.9%
9 543
 
0.5%
10 516
 
0.5%
Other values (68) 7792
 
7.6%
ValueCountFrequency (%)
1 63429
61.8%
2 14445
 
14.1%
3 6577
 
6.4%
4 3552
 
3.5%
5 1995
 
1.9%
6 1477
 
1.4%
7 996
 
1.0%
8 958
 
0.9%
9 543
 
0.5%
10 516
 
0.5%
ValueCountFrequency (%)
332 41
 
< 0.1%
327 469
0.5%
232 271
0.3%
218 29
 
< 0.1%
208 204
0.2%
186 152
 
0.1%
171 65
 
0.1%
161 116
 
0.1%
152 57
 
0.1%
126 54
 
0.1%

availability 365
Real number (ℝ)

Zeros 

Distinct438
Distinct (%)0.4%
Missing448
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean141.13325
Minimum-10
Maximum3677
Zeros23544
Zeros (%)22.9%
Negative432
Negative (%)0.4%
Memory size801.7 KiB
2025-04-11T13:20:49.715595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile0
Q13
median96
Q3269
95-th percentile365
Maximum3677
Range3687
Interquartile range (IQR)266

Descriptive statistics

Standard deviation135.43502
Coefficient of variation (CV)0.95962518
Kurtosis3.258822
Mean141.13325
Median Absolute Deviation (MAD)96
Skewness0.64302237
Sum14416903
Variance18342.646
MonotonicityNot monotonic
2025-04-11T13:20:49.785154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23544
 
22.9%
365 2500
 
2.4%
364 1168
 
1.1%
89 751
 
0.7%
1 734
 
0.7%
179 686
 
0.7%
90 676
 
0.7%
5 628
 
0.6%
3 579
 
0.6%
180 545
 
0.5%
Other values (428) 70340
68.6%
ValueCountFrequency (%)
-10 37
< 0.1%
-9 47
< 0.1%
-8 50
< 0.1%
-7 36
< 0.1%
-6 32
< 0.1%
-5 58
0.1%
-4 41
< 0.1%
-3 43
< 0.1%
-2 43
< 0.1%
-1 45
< 0.1%
ValueCountFrequency (%)
3677 1
 
< 0.1%
426 46
< 0.1%
425 50
< 0.1%
424 38
< 0.1%
423 48
< 0.1%
422 47
< 0.1%
421 42
< 0.1%
420 41
< 0.1%
419 44
< 0.1%
418 49
< 0.1%

house_rules
Text

Missing 

Distinct1976
Distinct (%)3.9%
Missing52131
Missing (%)50.8%
Memory size21.7 MiB
2025-04-11T13:20:50.172484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1001
Median length677
Mean length286.06345
Min length6

Characters and Unicode

Total characters14437050
Distinct characters228
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)0.1%

Sample

1st rowClean up and treat the home the way you'd like your home to be treated. No smoking.
2nd rowPet friendly but please confirm with me if the pet you are planning on bringing with you is OK. I have a cute and quiet mixed chihuahua. I could accept more guests (for an extra fee) but this also needs to be confirmed beforehand. Also friends traveling together could sleep in separate beds for an extra fee (the second bed is either a sofa bed or inflatable bed). Smoking is only allowed on the porch.
3rd rowI encourage you to use my kitchen, cooking and laundry facilities. There is no additional charge to use the washer/dryer in the basement. No smoking, inside or outside. Come home as late as you want. If you come home stumbling drunk, it's OK the first time. If you do it again, and you wake up me or the neighbors downstairs, we will be annoyed. (Just so you know . . . )
4th rowPlease no smoking in the house, porch or on the property (you can go to the nearby corner). Reasonable quiet after 10:30 pm. Please remove shoes in the house.
5th rowNo smoking, please, and no drugs.
ValueCountFrequency (%)
the 118068
 
4.6%
and 75765
 
2.9%
no 67657
 
2.6%
to 58130
 
2.3%
in 55345
 
2.1%
please 48102
 
1.9%
you 47063
 
1.8%
is 44901
 
1.7%
of 40136
 
1.6%
a 37787
 
1.5%
Other values (4783) 1988312
77.0%
2025-04-11T13:20:50.397175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2605513
18.0%
e 1433405
 
9.9%
o 950632
 
6.6%
t 888214
 
6.2%
a 786397
 
5.4%
s 749731
 
5.2%
n 740655
 
5.1%
i 693000
 
4.8%
r 657015
 
4.6%
l 488685
 
3.4%
Other values (218) 4443803
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14437050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2605513
18.0%
e 1433405
 
9.9%
o 950632
 
6.6%
t 888214
 
6.2%
a 786397
 
5.4%
s 749731
 
5.2%
n 740655
 
5.1%
i 693000
 
4.8%
r 657015
 
4.6%
l 488685
 
3.4%
Other values (218) 4443803
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14437050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2605513
18.0%
e 1433405
 
9.9%
o 950632
 
6.6%
t 888214
 
6.2%
a 786397
 
5.4%
s 749731
 
5.2%
n 740655
 
5.1%
i 693000
 
4.8%
r 657015
 
4.6%
l 488685
 
3.4%
Other values (218) 4443803
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14437050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2605513
18.0%
e 1433405
 
9.9%
o 950632
 
6.6%
t 888214
 
6.2%
a 786397
 
5.4%
s 749731
 
5.2%
n 740655
 
5.1%
i 693000
 
4.8%
r 657015
 
4.6%
l 488685
 
3.4%
Other values (218) 4443803
30.8%

license
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing102597
Missing (%)> 99.9%
Memory size3.1 MiB
2025-04-11T13:20:50.447217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41662/AL
2nd row41662/AL
ValueCountFrequency (%)
41662/al 2
100.0%
2025-04-11T13:20:50.537794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 4
25.0%
4 2
12.5%
1 2
12.5%
2 2
12.5%
/ 2
12.5%
A 2
12.5%
L 2
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 4
25.0%
4 2
12.5%
1 2
12.5%
2 2
12.5%
/ 2
12.5%
A 2
12.5%
L 2
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 4
25.0%
4 2
12.5%
1 2
12.5%
2 2
12.5%
/ 2
12.5%
A 2
12.5%
L 2
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 4
25.0%
4 2
12.5%
1 2
12.5%
2 2
12.5%
/ 2
12.5%
A 2
12.5%
L 2
12.5%

Interactions

2025-04-11T13:20:43.779042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:37.909548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:38.527574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:39.241181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:39.832185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:40.448209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:41.234378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:41.841394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:42.456418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:43.158514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:43.842096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:37.974103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:38.588125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:39.299731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:39.893737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:40.514265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:41.293428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:41.902946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:42.518971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:43.218065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:43.902647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:38.034154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:38.647676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:39.357280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:39.954787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:40.580321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:41.356482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:41.963999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-11T13:20:43.962698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:38.097208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:38.705726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:39.411326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:40.015841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-11T13:20:41.413030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:42.020046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:42.625562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-11T13:20:39.469876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:40.076392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:40.822527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:41.475082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:42.080598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:42.679107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:43.402722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:44.097313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:38.221313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:38.927414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:39.534431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:40.142949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:40.895089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:41.542640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:42.143651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:42.739158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:43.467777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:44.159867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:38.282365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:38.990968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:39.594482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:40.204001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:40.964148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:41.602190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:42.207705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:42.792704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:43.530831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:44.225422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-11T13:20:39.052020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-11T13:20:42.991873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:43.594885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:44.285473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:38.400966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:39.116075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:39.708579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:40.321600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:41.095260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:41.718289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:42.322804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:43.041915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:43.652935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:44.347526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:38.462018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:39.176627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:39.770131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:40.384655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:41.161315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:41.779842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:42.391362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:43.098964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:20:43.713486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-11T13:20:50.579830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Construction yearavailability 365calculated host listings countcancellation_policyhost idhost_identity_verifiedidinstant_bookablelatlongminimum nightsneighbourhood groupnumber of reviewsreview rate numberreviews per monthroom type
Construction year1.000-0.006-0.0050.0000.0050.0010.0010.0000.0040.001-0.0030.0090.0010.0150.0030.004
availability 365-0.0061.0000.2990.000-0.0030.003-0.1550.000-0.0010.0370.0350.0340.1360.0130.1650.047
calculated host listings count-0.0050.2991.0000.0020.0030.0000.0590.0000.0250.0460.0710.0880.0530.0220.1050.077
cancellation_policy0.0000.0000.0021.0000.0000.0000.0000.0060.0030.0000.0040.0020.0000.0000.0000.000
host id0.005-0.0030.0030.0001.0000.007-0.0010.0000.001-0.010-0.0000.003-0.0050.000-0.0030.005
host_identity_verified0.0010.0030.0000.0000.0071.0000.0000.0000.0000.0000.0000.0000.0060.0030.0000.000
id0.001-0.1550.0590.000-0.0010.0001.0000.000-0.0090.0400.0210.0480.0080.1370.0570.066
instant_bookable0.0000.0000.0000.0060.0000.0000.0001.0000.0020.0030.0000.0000.0000.0000.0000.003
lat0.004-0.0010.0250.0030.0010.000-0.0090.0021.0000.0330.0320.440-0.0520.014-0.0400.089
long0.0010.0370.0460.000-0.0100.0000.0400.0030.0331.000-0.1110.5390.0910.0240.1210.119
minimum nights-0.0030.0350.0710.004-0.0000.0000.0210.0000.032-0.1111.0000.006-0.1680.012-0.3240.008
neighbourhood group0.0090.0340.0880.0020.0030.0000.0480.0000.4400.5390.0061.0000.0230.0190.0290.094
number of reviews0.0010.1360.0530.000-0.0050.0060.0080.000-0.0520.091-0.1680.0231.0000.0200.7410.065
review rate number0.0150.0130.0220.0000.0000.0030.1370.0000.0140.0240.0120.0190.0201.0000.0050.008
reviews per month0.0030.1650.1050.000-0.0030.0000.0570.000-0.0400.121-0.3240.0290.7410.0051.0000.090
room type0.0040.0470.0770.0000.0050.0000.0660.0030.0890.1190.0080.0940.0650.0080.0901.000

Missing values

2025-04-11T13:20:44.483141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-11T13:20:44.712336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-11T13:20:45.198250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idNAMEhost idhost_identity_verifiedhost nameneighbourhood groupneighbourhoodlatlongcountrycountry codeinstant_bookablecancellation_policyroom typeConstruction yearpriceservice feeminimum nightsnumber of reviewslast reviewreviews per monthreview rate numbercalculated host listings countavailability 365house_ruleslicense
01001254Clean & quiet apt home by the park80014485718unconfirmedMadalineBrooklynKensington40.64749-73.97237United StatesUSFalsestrictPrivate room2020.0$966$19310.09.010/19/20210.214.06.0286.0Clean up and treat the home the way you'd like your home to be treated. No smoking.NaN
11002102Skylit Midtown Castle52335172823verifiedJennaManhattanMidtown40.75362-73.98377United StatesUSFalsemoderateEntire home/apt2007.0$142$2830.045.05/21/20220.384.02.0228.0Pet friendly but please confirm with me if the pet you are planning on bringing with you is OK. I have a cute and quiet mixed chihuahua. I could accept more guests (for an extra fee) but this also needs to be confirmed beforehand. Also friends traveling together could sleep in separate beds for an extra fee (the second bed is either a sofa bed or inflatable bed). Smoking is only allowed on the porch.NaN
21002403THE VILLAGE OF HARLEM....NEW YORK !78829239556NaNEliseManhattanHarlem40.80902-73.94190United StatesUSTrueflexiblePrivate room2005.0$620$1243.00.0NaNNaN5.01.0352.0I encourage you to use my kitchen, cooking and laundry facilities. There is no additional charge to use the washer/dryer in the basement. No smoking, inside or outside. Come home as late as you want. If you come home stumbling drunk, it's OK the first time. If you do it again, and you wake up me or the neighbors downstairs, we will be annoyed. (Just so you know . . . )NaN
31002755NaN85098326012unconfirmedGarryBrooklynClinton Hill40.68514-73.95976United StatesUSTruemoderateEntire home/apt2005.0$368$7430.0270.07/5/20194.644.01.0322.0NaNNaN
41003689Entire Apt: Spacious Studio/Loft by central park92037596077verifiedLyndonManhattanEast Harlem40.79851-73.94399United StatesUSFalsemoderateEntire home/apt2009.0$204$4110.09.011/19/20180.103.01.0289.0Please no smoking in the house, porch or on the property (you can go to the nearby corner). Reasonable quiet after 10:30 pm. Please remove shoes in the house.NaN
51004098Large Cozy 1 BR Apartment In Midtown East45498551794verifiedMichelleManhattanMurray Hill40.74767-73.97500United StatesUSTrueflexibleEntire home/apt2013.0$577$1153.074.06/22/20190.593.01.0374.0No smoking, please, and no drugs.NaN
61004650BlissArtsSpace!61300605564NaNAlbertaBrooklynBedford-Stuyvesant40.68688-73.95596United StatesUSFalsemoderatePrivate room2015.0$71$1445.049.010/5/20170.405.01.0224.0Please no shoes in the house so bring slippers or extra socks to keep your feet warm- especially in winter! No smoking either inside or outside. Please be considerate of neighbors from 10pm-7am in terms of noise. Please take out any trash and leave in the large blue garbage bin at the end of the driveway when you leave.NaN
71005202BlissArtsSpace!90821839709unconfirmedEmmaBrooklynBedford-Stuyvesant40.68688-73.95596United StatesUSFalsemoderatePrivate room2009.0$1,060$21245.049.010/5/20170.405.01.0219.0House Guidelines for our BnB We are delighted to welcome you. Check in Sun – Thurs by 8PM and Fri, Sat by 9pm. Please bear in mind that this is not a hotel but our home and we are opening it to you. We will do our utmost to make your stay enjoyable and fun. We ask that you take care to respect our home and its appearance. Thank you. Marilyn and Alan 1.ROOMS - The bedroom is yours for the duration of your stay. Be sure to let us know if you need something. Please keep it neat and tidy and take advantage of the closet and bureau for your belongings. We do ask that you turn off lights, air conditioner, fan, etc. when you are not in the room. Thank you. 2.LIGHTS – Please, too, remember to turn off lights on the stairway when returning. 3.KEYS - 2 house keys are provided - Replacement cost is $15 each 4.PARKING – Parking is available on street at all times. 5.SHOES – When the weather is inclement please remove shoes. 6.TOILETRIES - If you need them we can provide basic toiletrNaN
81005754Large Furnished Room Near B'way79384379533verifiedEvelynManhattanHell's Kitchen40.76489-73.98493United StatesUSTruestrictPrivate room2005.0$1,018$2042.0430.06/24/20193.473.01.0180.0- Please clean up after yourself when using the kitchen. - When using the bathroom, please be careful to minimize the amount of water on the floor when showering and using the sink. - Please make sure to remove any strands of hair from the sink and floors with a tissue or paper towel. - Be respectful of the noise levels after 11pm. - Please be quiet coming in late or leaving early. - No Smoking - No Pets - No overnight guests - For safety reasons, please make sure to shut and lock the windows and front door when you leave. *** Note: cancellations are subject to fees that are non refundable ***NaN
91006307Cozy Clean Guest Room - Family Apt75527839483unconfirmedCarlManhattanUpper West Side40.80178-73.96723United StatesUSFalsestrictPrivate room2015.0$291$582.0118.07/21/20170.995.01.0375.0NO SMOKING OR PETS ANYWHERE ON THE PROPERTY 1. Be respectful of the other tenants 2. If you use the grill, be sure to turn off the gas 3. Notify the owner of any discrepancies or things not functioning properly immediately 4. Please treat carpet stains promptly (or notify the owner) 5. Kindly turn off lights, air conditioners, and other electrical items when they aren't neededNaN
idNAMEhost idhost_identity_verifiedhost nameneighbourhood groupneighbourhoodlatlongcountrycountry codeinstant_bookablecancellation_policyroom typeConstruction yearpriceservice feeminimum nightsnumber of reviewslast reviewreviews per monthreview rate numbercalculated host listings countavailability 365house_ruleslicense
1025896089676Lrg room 1 block from Prospect Park74549151787unconfirmedDaveBrooklynFlatbush40.65231-73.96189United StatesUSFalseflexiblePrivate room2006.0$306$613.00.0NaNNaN1.01.0200.0House Rules 1. Check-in is 4 pm local time. If the unit is ready earlier, we’ll let you know. Check-out is normally 11 am local time, but we’d be happy to extend it as long as we don’t have a cleaning scheduled. Just let us know. 2. All bookings require a security deposit of at least $300, which will be refunded within 7 days of your check-out. 3. For security measures we require all guests to provide proof of identification through ID verification on our own website. In order to check in we'll need a photo of your ID. 4. Our cancellation policy is as shown on our ad and defined by the site you are booking through. 5. Don’t let $300 go up in smoke. There's no smoking allowed in any Flatbook and a $300 fine for breaking this rule. 6. Unfortunately we don’t allow pets in any of our apartments. 7. Unless you’re staying in one of our specialty apartments, we don’t allow parties or excessive noise. 8. If we find the place very messy, we have to charge an extra $40 for every extra hour oNaN
1025906090228Wonderful artists' loft in Brooklyn9184535139unconfirmedDanielBrooklynCrown Heights40.66673-73.96127United StatesUSTruemoderateEntire home/apt2003.0$250$501.00.0NaNNaN1.01.0276.0#NAME?NaN
1025916090781Columbus Ave Apt 1 block from Park50908010324verifiedLawrenceManhattanUpper West Side40.77408-73.98181United StatesUSFalsestrictEntire home/apt2005.0$1,139$2285.017.01/4/20190.355.01.0134.0#NAME?NaN
10259260913333BR/1 Ba in TriBeCa w/ outdoor deck53266862889unconfirmedNickManhattanTribeca40.71845-74.01183United StatesUSFalsemoderateEntire home/apt2016.0$787$1571.00.0NaNNaN2.01.0177.0Guests should treat my home as if it were their own. While my valuables are locked and kept away, I do ask that guests respect my private closet space and the items located within. Please, no smoking.NaN
1025936091885Welcoming, Clean, Cheap on St Marks33188605074verifiedFelipeManhattanEast Village40.72826-73.98422United StatesUSTruestrictPrivate room2017.0$1,099$2201.08.09/6/20150.164.02.0152.0* No smoking indoors. * No pets * No loud/large parties. * Please clean up before checking out. Any major clean-up service required may be deducted from the deposit. * Guests staying longer than one week are responsible for keeping the space tidy. We provide cleaning supplies. * Additional guests permitted for an extra charge per person per night (see price table).NaN
1025946092437Spare room in Williamsburg12312296767verifiedKrikBrooklynWilliamsburg40.70862-73.94651United StatesUSFalseflexiblePrivate room2003.0$844$1691.00.0NaNNaN3.01.0227.0No Smoking No Parties or Events of any kind Please take out your shoes when you arrive. Remember you are not a tenant you are a Guest of the Host.NaN
1025956092990Best Location near Columbia U77864383453unconfirmedMifanManhattanMorningside Heights40.80460-73.96545United StatesUSTruemoderatePrivate room2016.0$837$1671.01.07/6/20150.022.02.0395.0House rules: Guests agree to the following terms and conditions 1.Guest(s) agree to NO PARTIES anywhere on the property. Legal action will be taken for parties that cause damages to the unit, property, reputation, or relationship with building staff or other residents. 2.Smoking of any kind is not permitted in the unit or on the premises. 3.Guests agree to abide by maximum occupancy of unit, and observe quiet hours between 9pm and 9am 4.Guests are responsible for action of ALL GUESTS occupying their unit at all times. 5.Guests agree to return all keys, remotes, and parking passes. Replacement fees may apply for unreturned items 6.Guests understand that onsite building staff (maintenance and concierge) are not affiliated with Jordan or AIRBNB. Please contact me directly for any needed items. 7.Guests understand that no pets are to be in the unit. 8.Guests agree to pay replacement cost for any unreturned items, and $125 per hour fee for unauthorized early checkin or late checkout. 11NaN
1025966093542Comfy, bright room in Brooklyn69050334417unconfirmedMeganBrooklynPark Slope40.67505-73.98045United StatesUSTruemoderatePrivate room2009.0$988$1983.00.0NaNNaN5.01.0342.0NaNNaN
1025976094094Big Studio-One Stop from Midtown11160591270unconfirmedChristopherQueensLong Island City40.74989-73.93777United StatesUSTruestrictEntire home/apt2015.0$546$1092.05.010/11/20150.103.01.0386.0NaNNaN
1025986094647585 sf Luxury Studio68170633372unconfirmedRebeccaManhattanUpper West Side40.76807-73.98342United StatesUSFalseflexibleEntire home/apt2010.0$1,032$2061.00.0NaNNaN3.01.069.0NaNNaN

Duplicate rows

Most frequently occurring

idNAMEhost idhost_identity_verifiedhost nameneighbourhood groupneighbourhoodlatlongcountrycountry codeinstant_bookablecancellation_policyroom typeConstruction yearpriceservice feeminimum nightsnumber of reviewslast reviewreviews per monthreview rate numbercalculated host listings countavailability 365house_ruleslicense# duplicates
06026161Upper East Side 2 bedroom- close to Hospitals-65193709566verifiedJulianaManhattanUpper East Side40.76222-73.96030United StatesUSFalsemoderateEntire home/apt2008.0$105$2130.02.06/8/20190.213.034.0157.0NaNNaN2
16026714Close to East Side Hospitals- Modern 2 Bedroom Apt31072202372verifiedJulianaManhattanUpper East Side40.76249-73.96217United StatesUSFalsemoderateEntire home/apt2008.0$285$5730.06.01/31/20190.143.034.067.0The quieter the better, but otherwise make yourself at home!! Be as messy as you want - I have the place professionally cleaned when you leave, so towels, sheets, etc can all be left as-is, its no bother to me!NaN2
26027266ACADIA Spacious 2 Bedroom Apt - Close to Hospitals95854111798verifiedJulianaManhattanUpper East Side40.76021-73.96157United StatesUSFalsemoderateEntire home/apt2014.0$586$11730.010.011/18/20180.225.034.0211.0No Smoking No PetsNaN2
36027818*ENCHANTMENT* Upper East Side 2 bedroom- Sunny!73401481508unconfirmedJulianaManhattanUpper East Side40.76244-73.96031United StatesUSTruemoderateEntire home/apt2006.0$539$10830.09.09/30/20180.205.034.0411.0Please treat it as it were your own home and be respectful of the neighbors!NaN2
46028371*JAMES* Amazing Spacious 2 Bedroom- Bright!37678424985verifiedJulianaManhattanUpper East Side40.76035-73.96133United StatesUSFalseflexibleEntire home/apt2021.0$806$16130.08.06/11/20190.274.034.0411.0Be courteous and respectful to people in the house. We like to keep it clean and tidy, so your help in keeping your areas clean is much appreciated.NaN2
56028923Large Private Upper West Side Room48515749618verifiedMaxwellManhattanUpper West Side40.78567-73.97665United StatesUSFalseflexiblePrivate room2020.0$479$961.07.09/26/20150.143.01.0215.0NaNNaN2
66029475*ODYSSEY* Sunny 1 Bedroom Apt- Bright & Cheery!50618412686unconfirmedJulianaManhattanUpper West Side40.78181-73.98466United StatesUSTruestrictEntire home/apt2017.0$424$8530.010.04/1/20190.263.034.0129.0The property may be used only as a residence, not for any other purposes, commercial or otherwise (including filming). You may not have packages sent to you during your stay. Beacon Hill is a very quiet neighborhood, please keep the noise to a minimum and avoid playing loud music or making loud noises. If any noise complaints are received, I will assess a $100 fee from the deposit for each complaint. If inviting anybody over, please keep daytime occupancy to five people maximum. NO unregistered overnight guests are allowed. If you rearrange the furniture, please return everything to its original state before leaving. If bringing pets, please ensure that they are potty trained. Any pet stains left in the space will result in an additional $200 cleaning fee will be assessed from the deposit. No smoking. If any smoking occurs in the space, I will assess a $300 fee from the deposit. No illegal downloading. Do not use the internet to access, stream, or otherwise download any illegaNaN2
76030028*WINDSONG* Serene 1 BR in Townhouse near Park60449613505unconfirmedJulianaManhattanUpper West Side40.78289-73.98477United StatesUSTruestrictEntire home/apt2020.0$967$19330.017.06/17/20190.384.034.0164.0No Smoking No PetsNaN2
860305802BR Cozy, Large & Central Apartment45918690207unconfirmedPhilippeManhattanMidtown40.74462-73.98272United StatesUSTruestrictEntire home/apt2022.0$419$842.00.0NaNNaN3.01.0351.0NaNNaN2
96031132Nice room in a super nice apartment14162979052unconfirmedTerryBrooklynWilliamsburg40.71591-73.94170United StatesUSFalsestrictPrivate room2008.0$878$1767.02.08/16/20150.043.01.0367.0Please remove your shoes as you enter the apartment. Please respect my neighbors and honor my home – no parties, no "guests", no drugs, no orgies, no pets, no beer pong, no dinosaurs.NaN2